Interference cancellation is a key enabler of full-duplex radio communication. One particular type of interference is known as ‘self-interference’ which refers to interference from the transmit signal on to the receive path in the transceiver. The transmit power is high compared to the receive power, hence transmit signals at high power can dominate received signals in full-duplex radio communications. Self-interference cancellation enables lower cost components in the transceiver by relaxing the requirements on other cancellation components in full-duplex systems, thereby saving billions of dollars in mass produced transceivers for full-duplex radios. Self-interference cancellation techniques that are implemented in the digital domain can potentially cancel 20-25 dB or more of interference (originating from the transmit path) in the receive path.
An adaptive filter is a filter that self-adjusts the filter coefficients/weights according to a recursive algorithm, which enables the filter to perform satisfactorily in an environment where the statistics of the input signals are not available or are time-varying. An adaptive filter in the receive path of a full-duplex communication system cancels nonlinear interference arising from nonlinear components in the transmitter implementation. Recursive Least Squares (RLS) based algorithms attract a lot of interest in adaptive filtering applications due to its fast convergence speed. Recursive Least Squares (RLS) based algorithms recursively find filter coefficients that minimize the weighted linear least squares cost function relating to the input signals. However, the RLS algorithm offers extremely fast convergence at a cost of large area and power in its hardware implementation.